In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\final_buildings_output_1')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
# def find_max_building_storeys(gpr):
# max_building_storeys= 0
# if gpr >= 0 and gpr < 1.4:
# max_building_storeys = 5
# elif gpr >= 1.4 and gpr < 1.6:
# max_building_storeys = 12
# elif gpr >= 1.6 and gpr < 2.1:
# max_building_storeys = 24
# elif gpr >= 2.1 and gpr < 2.8:
# max_building_storeys = 36
# elif gpr >= 2.8:
# max_building_storeys = 48 ## by right got no limit
# return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [255, 10, 169]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [0,0,255]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,0]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]
gprs =[]
generated_gprs =[]
sanity_ratios =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
storey = matched_row['storeys'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
storey = int(storey)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = [0,255,0] # green
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = masked_rgb(simplified_gpr_value)
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
#max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*storey/actual_site_area
gprs.append(gpr_value)
generated_gprs.append(generated_gpr)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratios.append(ratio)
total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
absolute_error.append(abs(tar_gpr-gen_gpr))
square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 23065.1, Building pixels: 5645, Mask pixels: 16203, Generated GPR: 1.7419613651792878
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12, Site area: 37265.0, Building pixels: 1197, Mask pixels: 27439, Generated GPR: 0.5234884653230802
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36, Site area: 10414.2, Building pixels: 761, Mask pixels: 8554, Generated GPR: 3.202712181435586
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12, Site area: 6157.3, Building pixels: 154, Mask pixels: 4778, Generated GPR: 0.386772708246128
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15, Site area: 19547.0, Building pixels: 622, Mask pixels: 14355, Generated GPR: 0.6499477533960293
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 17455.9, Building pixels: 5207, Mask pixels: 12216, Generated GPR: 2.131221349050426
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15, Site area: 22094.4, Building pixels: 1236, Mask pixels: 16292, Generated GPR: 1.1379818315737786
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 10097.1, Building pixels: 474, Mask pixels: 7670, Generated GPR: 1.0505867014341592
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13564.8, Building pixels: 832, Mask pixels: 9962, Generated GPR: 1.4197952218430032
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18, Site area: 27418.2, Building pixels: 524, Mask pixels: 21829, Generated GPR: 0.4320857574785835
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:13, Site area: 17940.2, Building pixels: 1425, Mask pixels: 11806, Generated GPR: 1.5691173979332544
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:17, Site area: 13877.2, Building pixels: 920, Mask pixels: 9365, Generated GPR: 1.6700480512546716
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 7255.7, Building pixels: 2127, Mask pixels: 5237, Generated GPR: 2.030742791674623
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:19, Site area: 10502.8, Building pixels: 234, Mask pixels: 8276, Generated GPR: 0.5372160463992266
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Storeys:18, Site area: 13000.3, Building pixels: 1684, Mask pixels: 9208, Generated GPR: 3.2919200695047786
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13241.8, Building pixels: 926, Mask pixels: 9680, Generated GPR: 1.6262396694214876
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:16, Site area: 39401.6, Building pixels: 553, Mask pixels: 28711, Generated GPR: 0.3081745672390373
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:15, Site area: 28692.65, Building pixels: 350, Mask pixels: 20526, Generated GPR: 0.2557731657410114
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:16, Site area: 18747.8, Building pixels: 534, Mask pixels: 13058, Generated GPR: 0.6543115331597489
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Storeys:19, Site area: 14344.0, Building pixels: 704, Mask pixels: 10510, Generated GPR: 1.2726926736441484 Accuracies:[0.24425811798520564, 0.7906046138707679, 0.14382577908413796, 0.75826705734617, 0.7833507488679903, 0.5223009636074472, 0.593577917295079, 0.6498044328552802, 0.5267349260523323, 0.7942448773911508, 0.43960092930955197, 0.4035542674090458, 0.45053056548187354, 0.7441828350479873, 0.059451408712920414, 0.45792011019283746, 0.8532502060766489, 0.8782032544090422, 0.781896155613417, 0.6256786253987799] Square error:[0.11693757527528229, 3.9065978467109126, 0.16217710107660857, 1.471920461456435, 5.522745561768371, 0.534684661307125, 2.762304392178851, 3.800212208625352, 2.4970471409102033, 2.781937920405791, 1.5150719800706018, 1.276791406473365, 0.3978364692494967, 2.4422936856320647, 0.043297257474896184, 1.8872174458711837, 3.210638381489012, 3.4011726162009315, 5.502254383467367, 4.525436460767282] Absolute error:[0.3419613651792879, 1.9765115346769198, 0.4027121814355863, 1.213227291753872, 2.350052246603971, 0.731221349050426, 1.6620181684262212, 1.9494132985658408, 1.5802047781569968, 1.6679142425214166, 1.2308826020667454, 1.1299519487453282, 0.6307427916746229, 1.5627839536007735, 0.20807993049522144, 1.3737603305785124, 1.7918254327609628, 1.8442268342589887, 2.345688466840251, 2.1273073263558517] Accuracy:0.5750618896003832 MAE:1.4060243036873898 RMSE:1.545292447344695